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In this paper we present an approach to automatically segmenting non-rigid pedestrians in still images. Inspired by global shape matching as well as interactive figure-ground separation methods, this approach fulfills the task combining shape and appearance cues in a unified framework. The main idea is to initially extract pedestrian silhouette and skeleton via hierarchical shape matching, and then generate an appearance trimap to refine segmentation. The major contributions of this paper include: 1) a novel shape matching scheme, which is proposed to replace the commonly used Chamfer matching in the shape matching stage, 2) a head-torso parsing method, which is developed for localizing pedestrian to reduce the search space, 3) an automatic trimap generation method used to refine segmentation. Experiments on public datasets demonstrate that the approach improves pedestrian segmentation efficiently and effectively.